Evaluation Questions: Do characters of the same GOT houses share similar personality traits? Are there more ‘strong’ female or male characters in GOT? Do ‘strong’ characters hold the most power in Westeros?

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

library(tidytuesdayR)

tuesdata <- tidytuesdayR::tt_load('2022-08-16')
#tuesdata <- tidytuesdayR::tt_load(2022, week = 33)

characters <- tuesdata$characters
myers_briggs <- tuesdata$myers_briggs
psych_stats <- tuesdata$psych_stats

characters %>%
  filter(
    uni_id == 'GOT'
  ) -> characters_got

myers_briggs %>%
  filter(
    uni_id == 'GOT'
    ) -> myers_briggs_got

psych_stats %>%
  filter(
    uni_id == 'GOT'
  ) -> psych_stats_got

characters <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-08-16/characters.csv')
psychometrics <-  readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-08-16/psych_stats.csv')

Data wrangling

# Network to see similarities between GOT characters?

# set seed
set.seed(4321)

Intersection

psycho_df <- psychometrics %>%
             dplyr::filter(uni_name == "Game of Thrones" & avg_rating >= 80) %>% 
             dplyr::group_by(char_name, question) %>%
             dplyr::slice_max(order_by = avg_rating, n = 1, with_ties = FALSE) %>% 
             dplyr::ungroup() %>% 
             dplyr::group_by(question) %>%
             dplyr::mutate(question_id = dplyr::cur_group_id(),
                           personality = stringr::str_c(question_id, personality, sep = "_")) %>% 
             dplyr::ungroup() %>%
             dplyr::select(char_name, question, personality) %>% 
             dplyr::group_by(char_name) %>%
             dplyr::summarise(personality = list(as.character(personality))) %>%
             dplyr::mutate(house = case_when(
               str_detect(char_name, 'Stark') ~ 'Stark',
               str_detect(char_name, 'Snow') ~ 'Stark',
               str_detect(char_name, 'Greyjoy') ~ 'Greyjoy',
               str_detect(char_name, 'Tarth') ~ 'Tarth',
               str_detect(char_name, 'Bronn') ~ 'Blackwater',
               str_detect(char_name, 'Lannister') ~ 'Lannister',
               str_detect(char_name, 'Targaryen') ~ 'Targaryen',
               str_detect(char_name, 'Seaworth') ~ 'Seaworth',
               str_detect(char_name, 'Baratheon') ~ 'Baratheon',
               str_detect(char_name, 'Mormont') ~ 'Mormont',
               str_detect(char_name, 'Tyrell') ~ 'Tyrell',
               str_detect(char_name, 'Martell') ~ 'Martell',
               str_detect(char_name, 'Baelish') ~ 'Baelish',
               str_detect(char_name, 'Tarly') ~ 'Tarly',
               str_detect(char_name, 'Clegane') ~ 'Clegane'
             )) %>% 
             dplyr::rename("from" = "char_name") %>% 
             tidyr::crossing(dplyr::rename(., "to" = "from", "personality_to" = "personality")) %>% 
             dplyr::mutate(frequency = purrr::map2_dbl(personality, personality_to, ~length(base::intersect(.x, .y)))) %>% 
             dplyr::select(from, to, frequency) %>% 
             dplyr::filter(from != to) %>% 
             dplyr::mutate(similarity = case_when(frequency <= quantile(frequency, probs = 0.80) ~ "q80",
                                                  frequency <= quantile(frequency, probs = 0.90) ~ "q90",
                                                  frequency <= quantile(frequency, probs = 0.95) ~ "q95",
                                                  frequency <= quantile(frequency, probs = 0.99) ~ "q99",
                                                  TRUE ~ "Top1")) %>% 
             dplyr::arrange(frequency)

Network

Network vertices

psycho_ve <- psycho_df %>%
             dplyr::group_by(to) %>%
             dplyr::summarise(links = sum(frequency)) %>%
             dplyr::select(to, links)

Network object

psycho_ig <- igraph::graph_from_data_frame(d = psycho_df, 
                                           vertices = psycho_ve, 
                                           directed = FALSE)

Network tidy

psycho_tg <- tidygraph::as_tbl_graph(psycho_ig) %>% 
             tidygraph::activate(nodes) %>% 
             dplyr::mutate(label = name)

size <- igraph::V(psycho_tg)$links

Legend Annotation

data_label <- tibble(x = seq(1.75, 2.25, length.out = 30),
                     y = seq(-1.35, -1.35, length.out = 30),
                     color  = psycho_ve %>% pull(links) %>% sort(),
                     alpha  = psycho_ve %>% pull(links) %>% sort(),    
                     size   = psycho_ve %>% pull(links) %>% sort())

Plot aesthetics

background     <- "#141C26"
lines_color    <- "#A6444C"
title_color    <- "#F2C230"
subtitle_color <- "#FFAB16"
text_color     <- "#F2F2F2"
caption_color  <- "#6C7D8C"

Annotation

annotation_title_text <- c("Game of Thrones")
annotation_text <- c("The Open-Source Psychometrics Project, it has recruited more than 3 million volunteers to rate characters from different series, according to 400 descriptive adjectives and other properties. The responses can be aggregated to create profiles that users can match as part of a personality test. In total, the project collects information on more than 2,000 characters, with 400 descriptive adjectives rated on a scale of 1 to 100, depending on how strongly one would identify the character's particular characteristic. From the data set, GOT characters have been selected, to investigate which characters share the greatest number of the strongest characteristics (rating >80%) in common or similarity.")
annotation_text <- stringr::str_wrap(annotation_text, 70) %>% 
                   stringr::str_replace_all("\n","<br>")

Plot

Plot <- psycho_tg %>%
        ggraph::ggraph(layout = "igraph", algorithm = 'fr') +
        ggraph::geom_edge_arc(aes(edge_width = similarity, alpha = similarity, colour = similarity), lineend = "round", strength = .1) +
        ggraph::geom_node_point(aes(colour = links), fill = background, size = log(size) * 1.5, alpha = 1.0, shape = 21) +
        ggraph::geom_node_point(aes(colour = links), size = log(size) * 1.0, alpha = 1.0) +
        ### Annotations ###
        ggraph::geom_node_label(aes(label = label), colour = text_color, size = log(size) * 0.60, family = "Cinzel Decorative", fontface = "bold", repel = FALSE, nudge_y = -0.04, nudge_x = 0.00, alpha = 0.6, fill = background, label.size = NA) +
        ggtext::geom_richtext(aes(x = 1.26, y = -1.33), label = annotation_text, color = subtitle_color, size = 4, family = "Cinzel", face = "plain", fill = "transparent", label.size = NA, hjust = 0.5, halign = 0, vjust = 0.5, valign = 0.5, margin = margin(t = 0.1, r = 0.1, b = 0.5, l = 0.1, unit = "cm")) +
        ### Scales ###
        ggraph::scale_edge_width_manual(values = c(seq(0.2, 1.0, length.out = 4), 2.0), breaks = c("q80","q90","q95","q99","Top1")) +
        ggraph::scale_edge_alpha_manual(values = c(seq(0.2, 0.5, length.out = 4), 0.8), breaks = c("q80","q90","q95","q99","Top1")) +
        ggraph::scale_edge_colour_manual(values = viridis::inferno(n = length(unique(psycho_df$similarity)), begin = 0.5, end = 0.9, direction = 1)) +
        scale_colour_gradientn(colors = viridis::plasma(n = length(unique(psycho_ve$links)), begin = 0.5, end = 0.9, direction = 1)) +
        guides(edge_width = "none", edge_alpha = "none", colour = "none", edge_colour = "none") +
        coord_cartesian(ylim = c(-1.4, 0.2)) +
        ### Theme ###
        theme_void() +
        theme(
          ## Plot Aesthetic ##
          panel.background = element_rect(fill = background, color = NA),
          plot.background  = element_rect(fill = background, color = NA),
          ## Titles & Caption ##
          plot.title.position = "panel",
          plot.title = ggtext::element_markdown(color = title_color, family = "Cinzel", face = "plain", size = 42, hjust = 0.5, halign = 0.5, vjust = 0.5, valign = 0.5, margin = unit(c(0.1, 0.1, 0.1, 0.1), "cm")),
          plot.caption.position = "panel",
          plot.caption = ggtext::element_markdown(color = caption_color, family = "Menlo", size = 10, hjust = 1, vjust = 0, margin = margin(t = -0.5, r = 0.1, b = 0.1, l = 0.0, unit = "cm")),
          ## Margin ##
          plot.margin = margin(t = 0.5, r = 1.5, b = 0.5, l = 1.5, unit = "cm")) +
        ### Labels ###
        ggplot2::labs(x = "",
                      y = "",
                      title = annotation_title_text,
                      caption = "<span style='font-family: \"Font Awesome 6 Brands\"'>&#xf099;</span> @TamayoLeiva_J<br>
                                 <span style='font-family: \"Font Awesome 6 Brands\"'>&#xf09b;</span>  TamayoLeivaJ<br><br> 
                                 Source: Open-Source Psychometrics Project courtesy of <span style='font-family: \"Font Awesome 6 Brands\"'>&#xf099;</span> @tanya_shapiro")


Plot +
              ### Legend Annotation ###
              ggforce::geom_link2(data = data_label, aes(x = x, y = y, colour = color, alpha = alpha, size = size, group = 1), lineend = 'round') +
              geom_point(data = data_label[c(1, 30),], aes(x = x, y = y, colour = color), fill = background, alpha = 1.0, size = range(log(size) * 1.5), shape = 21) +
              geom_point(data = data_label[c(1, 30),], aes(x = x, y = y, colour = color), alpha = 1.0, size = range(log(size))) +
              geom_text(data = data_label[c(1, 30),], aes(x = x, y = y + 0.002), label = c("-","+"), colour = background, alpha = 1.0, size = sort(log(size))[c(1,30)], family = "Cinzel Decorative", fontface = "bold", hjust = 0.5, vjust = 0.5) +
              geom_text(data = data_label[15,], aes(x = x, y = y + 0.100), label = "How to read the network?", colour = subtitle_color, alpha = 1.0, size = sort(log(size) * 1.5)[1], family = "Cinzel", fontface = "plain") +
              geom_text(data = data_label[15,], aes(x = x, y = y + 0.025), label = "Connections Scale", colour = text_color, alpha = 1.0, size = sort(log(size) * 0.8)[1], family = "Cinzel Decorative", fontface = "bold") +
              geom_text(data = data_label[15,], aes(x = x, y = y - 0.025), label = "Similarity Scale", colour = text_color, alpha = 1.0, size = sort(log(size) * 0.8)[1], family = "Cinzel Decorative", fontface = "bold") +
              scale_alpha_continuous(range = c(0.0, 1.0)) +
              scale_size_continuous(range = c(0.2, 2.0)) +
              guides(colour = "none", alpha = "none", size = "none")